How Prompt Engineering Inventor Built $1.5B in 3 Years | You.com, Richard Socher

Intro

00:00had the crazy idea in 2010 to use neural networks for natural language processing which was a very controversial idea at the time. A lot of people in the field said, "Oh, neural networks, they never work. They won't ever work." Most of my papers got rejected cuz people hated neural nets for NLP.

00:13MIT and especially Berkeley here in the Bay Area, they hated neural networks for natural language processing too. We had invented prompt engineering and so we thought people should get access to this. We felt like well someone's got to do it.

00:26In 2020 when we started.com a lot of people said search [music] is dead. nothing you can do. But I don't really generally care about what's popular. I just care about what's meaningful. When [music] you once you really love an idea and you feel like that idea makes sense from first principles, you have to have a little bit of that belief inside of you that [music] you can make it prevail through a lot of rejection and still keep on going.

00:45Hi everyone, I'm Richard Ser. I'm the founder and CEO of.com and the founder and partner at AI Accentur. U.com is AI search infrastructure. In order to make an LM not hallucinate, you actually need to have a good search infrastructure to inform that LM.

01:02People searched on Google AI and LMS and agents search on you.com. Companies like OpenAI, Amazon, Alibaba, Telegraph, Windsurf, Harvey use our API infrastructure to make their LMS upto-date, accurate, and have citations. So you can actually verify that the facts are correct.

01:25It's amazing to become a unicorn. And in many ways, it also just feels like okay, it's what's next? What's the next step?

How the Inventor of Prompt Engineering Built a $1.5B Unicorn

01:43I originally from Germany. I thought a lot about sort of the meaning of life when I was younger. In high school already, I love natural languages. You know, I studied English. I'm originally from Germany and [music] French and Chinese and but I also love math.

01:55And so math and languages don't intersect often, right? It's very different fields of study, but they do intersect [music] in a computer where you try to use math to understand language. And so I ended up deciding study linguistic computer science in 2003.

02:12[music] It was definitely not famous or or popular in Germany. Linguistic computer science as it was called then was very much a niche orchid type of subject that very few people were studying. Still remember my my dad thinkingh what what will become of my son cuz this linguistic computer science thing doesn't sound like anything useful for ever like uh for a long time and but I don't really generally care about what's popular.

02:35I just care about what's meaningful. And

Why Linguistics is the Operating System of Intelligence

02:39then it felt to me like if we could really get that to work uh on the research side, it would have an amazing impact. Ultimately, language is the most interesting manifestation of human intelligence. Several civilizations were able [music] to create written language to not all of them, right?

02:53And the ones that didn't were falling behind and we're saying similar things. Civilizations that don't use AI now are falling behind. And so ultimately I think it's very meaningful to help [music] us understand language because it helps us understand who we are as humans.

03:08And then it uh also uh [music] actually helps in on the journey to understanding intelligence. It helps to create it because anything we can create we can engineer we understand a lot better afterward. Your brain and mine are jam-packed full of neurons that are tightly connected to and they talk to each other.

03:24in a computer. We can therefore build what's called an artificial neuronet network or the the other technical term is a sponse learning algorithm. But we can build a neuronet network that simulates all of these neurons being connected to and talking to each other.

03:38And then I was very fortunate uh at Stanford to hear Andrew talk about deep learning and neural nets for computer vision. It made sense to me from first principles that neural networks would be right because a lot of the research actually was about feature engineering that were people were doing at the time like in sentiment analysis you might say oh these are positive words and this is how negation works and here's like all these like linguists would come up with features and that clearly wouldn't scale to more complex things like translation [music] and I wanted to unify also uh the field

04:12and that eventually led us to prompt engineering and inventing that but then

The Moment We Had to Scale Up

04:15after [music] the PhD I was like now we have the main ingredients. We know how to make it work. We need large neural networks. We need a lot of data. And I showed that in all my research papers. And now we [music] need to actually scale it up.

04:25We need to like take those ideas and apply them into real applications for real people. And I think a lot of the papers that came out in the last couple of years, they made everything a little bit better. But those main ideas of endtoend trainable neural networks on large data sets, that is the main idea.

04:41That's [music] those those ingredients are the main ideas that push the field forward. And I felt like it made more [music] sense now to majorly scale that. And then in academia, you just don't have the resources to really scale. And then [music] while I was excited about scaling it, I should have scaled it even more.

04:57You know, I thought, oh, I raised like 1020 million, but I should have raised $200 million or a billion [music] dollars to really scale it more. So it was clear to me that to really scale it, you had to do it in industry. And that the technology was ready now to move out of academic research into the real world.

05:13And then when you maximize impact and you realize well the main ideas have we've been researched now it's time to really apply them in the real world and so startup uh felt like it made sense.

Solving for Impact When Everyone Said No

05:29So I graduated in 2014 then started [music] Metammind. Metammind uh was basically an AI platform that made it very easy to train neural networks. we had [music] uh started selling it to but for selling you had to be very very focused on one small niche but we had built this very powerful platform and so felt like in the hands of a no pun intended sales [music] force that is very large we could actually have much much more impact and the impact within Salesforce was much much larger for the way we had built that company and then I thought for a long time like I'll just

05:58be very happy cuz [music] uh Salesforce and do amazing research and improve a lot of the products we did not [music] just prompt engineering but we also built the are just language model for proteins for instance and biology. And then we had invented prompt engineering.

06:11And so [music] we trained this one neural network that can give you all different kinds of answers. And so we thought clearly people should get access to [music] this. and we published a paper and you know the paper got cited by other people at OpenAI and Alec Ratford and Ilia and they said oh this is an interesting idea and they extended it and and so on but we felt like well they're also a research lab so we needed to bring this to real people and Google was just not doing anything cuz they're a monopoly they're making money and [music] more and more money just selling

06:39advertisement and so they didn't see a need reason and weren't making any interesting [music] sort of modifications to fundamentally how we search and so we felt like well someone's has got to do it. And so we we

The Beginning of You.com: Challenging Google

06:51started [music] you.com and we felt like it had to be a new company to have the impact to really become a better way of finding information online. And eventually we became the first to put an LM into a search engine. Imagine you know Google Gemini like people also ask and you get like answers from AI.

07:07We did those kinds of things but in 2021. So it is different because it was no no one that didn't exist you know a research background where you the whole idea of being a PhD [music] is to do things that don't exist right to create new ideas and and new models uh and and then you can often think about what kinds of new ideas and models should you build the kinds of [music] things that are useful for people and when you ask like oh how do I write a Fibonacci function or how do I write an HTML page that does something it's just obvious that it's better to just get an

07:43answer from an LM than to guess get a list of blue links where you then have to click on 10 open tabs and open them up and then kind of uh find the answer somewhere else. Uh and so that just seems like from first principles it's better to get an answer than lists of links that may have the answer or not.

08:00So that's how we invented that one.

Build What People Will Actually Pay For

08:08I think the biggest thing for us was the pivot into enterprise. That was a really good focus. A lot of folks now realize they need AI, but only the experts realize that in order to make AI accurate, in order to make an LM not hallucinate, you actually need to [music] have a good search infrastructure to inform that LM.

08:26So, we built that infrastructure layer cuz we've been at [music] it since 2022. What we found is more and more companies actually want to use the underlying infrastructure for their own solutions inside [music] their own products. I guess you know there's sort of different pivots in the world right you can say oh we're make cameras and now we sell soft [music] drinks right that's a big pivot but we gave people answers and now we [music] give people answers but how we're selling those answers is different and it's good to follow the revenue here are a bunch of people who want to use

09:02the product for free and then here a bunch of people who want to use and get really good answers over their own custom data sets and they're willing to pay you follow the people that pay follow real revenue, not like, okay, hype. Some people say, oh, I want to use this product for free, and you're like, okay, that's great.

09:17But if you build something that uh companies are willing to pay for, you know, you've built something of value.

Are You Moving Fast Enough to Lead the AI Era?

09:29Some people think we should like slow down. I think we should accelerate more. I think we should accelerate everything a lot more. It's kind of interesting. It's hard to navigate AI because on the one hand there's real impact, right? Our customers have built over 100,000 agents that are automating real tasks for their work, right?

09:46And [music] they're telling us and they're paying us for it because it's valuable and it's it works. At the same time, there's a lot of hype and around AI like how quickly and how far are we on super intelligence? Uh are we on the right track for that?

09:59How much could uh a browser automate complete tasks without knowing enough about me and things like [music] that? And sometimes the timelines are a little bit off. You know, maybe it will take a little bit longer, but the field moves so quickly, you have to mostly think of like 2 to 4 week cycles to try to move quickly.

10:19Okay. And so it's important to think about what are the right applications where you can create ideally virtuous data cycles where you do something manually. You collect data and then you make that decision process a little bit better and then at some point you've made it good enough that you can automate it.

10:36And so I for instance didn't [music] want to invest in a bunch of self-driving car companies that said we don't even need a steering wheel. [music] We just need to like have a full self-driving car. And I was like, "Oh man, you can't sell that car until you're perfect."

10:49And so that's not generally good. AI [music] is not right away perfect. Humans aren't perfect, right? Humans still make driving mistakes and so on. AI will make some driving mistakes, too. And so it's good to have a steering [music] wheel. And so the companies that were able to eventually get to full self-driving were either the really clever ones like Tesla.

11:06You buy the car, you pay for the product, you use the product, you're now creating training data by using the product. And then the eye can use that training data and eventually [music] automate uh the process. When you [music] see small but continuous improvements, that's when you you can, you know, be very motivated too.

11:27So that's one of my mottos is better, better, never done, right? You can always improve uh yourself, your company, your processes. Overall, I would summarize it as excitement. I love AI. I love AI in all of its facets from foundational research and thinking about the upper bounds of super intelligence uh all the way down to like how do we make it really work now and get it into the hands of more companies and people to like to make their lives better.